AI RESEARCH
Loss-Conditional PINNs for Parametric PDE Families
arXiv CS.LG
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ArXi:2606.04420v1 Announce Type: new Physics-informed neural networks (PINNs) approximate solutions of ODEs and PDEs by minimising a weighted combination of residual, boundary, initial, and data losses. Their performance is often dominated by the choice of loss weights: a poor weighting can drive